ConnectSphere Appliance
A rack unit that ships before your GPU procurement clears
Pre-configured GPU server with the ConnectSphere stack and a local LLM installed, integrated by a local German hardware vendor. Operational in 4–6 weeks* instead of the 12–18 months a customer-side GPU procurement typically takes. The deployment option for customers who need both data sovereignty and time-to-value.
Why an appliance
The procurement bottleneck
Most enterprise AI initiatives stall in a recognizable pattern. The ambition is set in Q1, budget is approved in Q2, then the GPU procurement clock starts. Six months pass, then twelve, while IT, security, and procurement work through enterprise channels for a class of hardware that's globally back-ordered. The data team spends those months in scoping meetings.
Customer-side GPU procurement
- Budget approval
- Vendor selection
- Security review
- Networking review
- Procurement order
- Delivery
- Install and provision
- Validate
12–18 months
ConnectSphere Appliance
- Sizing conversation
- Configure and order
- Integrate with the partner
- Ship and rack
- Connect to source systems
- Operational
4–6 weeks*
The Appliance doesn't change what gets built — the architectural work is the same in any deployment mode. It removes the hardware-procurement obstacle so the architecture work can actually start. More on the structural argument: The AI adoption gap.
What's in the box
Three GPU configurations on one platform
One ConnectSphere Appliance, three pre-built GPU configurations. Sized to your workload during the sizing conversation; software stack and methodology are identical across configurations.
1× GPU
1 × Nvidia Blackwell 5000
48 GB VRAM
Pilot deployments, focused workloads, evaluation environments.
2× GPU
2 × Nvidia Blackwell 5000
96 GB total VRAM
Standard production workloads with concurrent grounding strategies.
4× GPU
4 × Nvidia Blackwell 5000
192 GB total VRAM
Higher-throughput environments, larger model serving, multi-tenant workloads.
Common across configurations
Built and integrated in Germany
Hardware integration, configuration, and warranty are handled by our local integration partner in Germany. The German integration partnership is an architectural choice, not a regional limitation: data, model weights, inference, and the hardware supply chain all stay inside the EU — reinforcing the same sovereignty story the Appliance delivers at the software layer.
Software pre-installed
Meta-Architecture Overlay
The non-invasive overlay platform that reads source systems where they live.
Normalization Engine
Cardinality-driven analysis that produces the 3NF substrate without domain-modeling workshops.
Semantic Dictionary
Versioned mapping from legacy field names to business terms — what makes the substrate readable to humans and LLMs.
Skills Engine
Procedural in-context learning over the substrate, with a local LLM running on-box.
Audit & Provenance
Lineage from every reported value back to a specific source row, with timestamp and stewardship metadata.
Deployment options
Where the Appliance fits
ConnectSphere supports four deployment modes for the inference layer. The keystone — normalization, Skills, audit — is identical across all four; only where the LLM runs changes. The Appliance is the recommended mode when sovereignty and time-to-value both matter.
| Mode | Best for | Sovereignty | Time to operational |
|---|---|---|---|
| ConnectSphere Appliance | Blocked by GPU procurement, sovereignty + speed | Highest | 4–6 weeks* |
| BYO on-prem hardware | Existing GPU capacity in your data center | Highest | Existing |
| Private cloud | Cloud-standardized enterprises (Azure ML, AWS Bedrock) | Medium (regional / VPC-isolated) | Days–weeks |
| Direct API | Pilots, fastest start, no infra commitment | Lowest | Hours |
Customers often combine modes — Appliance for production with sovereign data, direct API for early prototyping, private cloud for non-regulated workloads. The substrate is the same; the inference target is a deployment choice.
Order to operational
From sizing call to running methodology
The Appliance compresses the procurement window without changing the methodology. Once it's racked and connected, the same six- phase work happens that would happen on BYO hardware or in private cloud — just without the 12-month wait first.
Weeks 1–4
Order, configure, ship
Sizing conversation, configuration finalized with the integration partner, hardware build and burn-in, shipped.
Weeks 5–6
Rack, network, ingest
Appliance racked in your data center, networking configured, source-system credentials provisioned, read-only ingestion begins.
Months 2–6
The methodology runs
Cardinality observation, normalization, single logical truth layer, AI enablement — the same six-phase methodology that runs on any other deployment.
For the methodology that runs in months 2–6, see Our approach and How a normalized data foundation actually gets built.
What it enables
What you get with the Appliance
The Appliance is a deployment vehicle, not the product. The product is the keystone — the normalized substrate that any grounding strategy can rely on. The Appliance just gets that substrate operational sooner, with stronger sovereignty guarantees, on hardware that's already in your rack.
Sovereignty by construction
Data, model weights, inference, and hardware integration all stay in the EU. Aligned with the EU Data Act's structural guarantees.
Data sovereignty & the EU Data Act →Audit-readiness inherited
The substrate's audit trail extends to every AI output. Compliance reviews stop being archaeology.
Why audit-readiness has to be structural →6-month POC credibility
The 6-month timeline is hard to commit to when hardware procurement is in the critical path. With the Appliance, hardware isn't on the critical path.
Our approach in depth →Grounding stack works on day one
RAG, fine-tuning, Skills, and tool use all consume the same normalized substrate the moment ingestion completes.
Why every grounding technique needs the same thing underneath →Ready to size an Appliance for your environment?
Start with a 30-minute diagnostic to map your data landscape. We'll size the GPU configuration, scope the 6-month POC, and quote against your specific source-system mix.
* Lead times depend on current GPU supply-chain conditions, which are strained as of 2026; the sizing conversation includes current delivery windows for the selected configuration. Pricing is configuration- dependent. Hardware delivery and integration by a local German vendor.